{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:24:37Z","timestamp":1774405477344,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T00:00:00Z","timestamp":1533081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p &lt; 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.<\/jats:p>","DOI":"10.3390\/s18082497","type":"journal-article","created":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T11:22:34Z","timestamp":1533122554000},"page":"2497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":191,"title":["Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6141-3648","authenticated-orcid":false,"given":"Muhammad","family":"Zia ur Rehman","sequence":"first","affiliation":[{"name":"Department of Robotics &amp; Artificial Intelligence, School of Mechanical &amp; Manufacturing Engineering, National University of Sciences &amp; Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Asim","family":"Waris","sequence":"additional","affiliation":[{"name":"Department of Robotics &amp; Artificial Intelligence, School of Mechanical &amp; Manufacturing Engineering, National University of Sciences &amp; Technology (NUST), Islamabad 44000, Pakistan"},{"name":"Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9200 Aalborg, Denmark"}]},{"given":"Syed Omer","family":"Gilani","sequence":"additional","affiliation":[{"name":"Department of Robotics &amp; Artificial Intelligence, School of Mechanical &amp; Manufacturing Engineering, National University of Sciences &amp; Technology (NUST), Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7729-4359","authenticated-orcid":false,"given":"Mads","family":"Jochumsen","sequence":"additional","affiliation":[{"name":"Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9200 Aalborg, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8752-7224","authenticated-orcid":false,"given":"Imran Khan","family":"Niazi","sequence":"additional","affiliation":[{"name":"Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9200 Aalborg, Denmark"},{"name":"Center for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand"},{"name":"Health and Rehabilitation Research Institute, AUT University, Auckland 1142, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8835-2451","authenticated-orcid":false,"given":"Mohsin","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of Robotics &amp; Artificial Intelligence, School of Mechanical &amp; Manufacturing Engineering, National University of Sciences &amp; Technology (NUST), Islamabad 44000, Pakistan"},{"name":"Department of Electrical Engineering, Faculty of Engineering, Islamic University Medina, Medina 41411, Saudi Arabia"}]},{"given":"Dario","family":"Farina","sequence":"additional","affiliation":[{"name":"Department Bioengineering, Imperial College London, London SW72AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-2090","authenticated-orcid":false,"given":"Ernest Nlandu","family":"Kamavuako","sequence":"additional","affiliation":[{"name":"Centre for Robotics Research, Department of Informatics, King\u2019s College, London WC2G4BG, UK"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,1]]},"reference":[{"key":"ref_1","unstructured":"Scott, R. 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